It wasn’t long ago that one of the main challenges that retail marketers faced was the collation of relevant information about customers and their shopping patterns. Now, with the explosion of data sources – from loyalty data to social interactions, our recent survey found that 40% of retailers face the challenge of integrating data across multiple marketing channels. So, whether you’re in grocery retail, health & beauty or pet specialty, it’s time to fully embrace the marriage of retail marketing and AI.
With 90% of retailers currently executing personalized marketing campaigns, it’s clear that delivering these campaigns efficiently is a struggle, which leads to the question – how effective are those campaigns?
Retail marketing and AI: They just belong together
Personalized marketing campaigns can only be successful if the retail marketer has a 360° view of the customer, and the ability to recommend the most relevant offers to a given customer at a given time. There is little point having all the data if the marketer isn’t able to tap into it efficiently. This is where AI and machine learning play an essential role – there is no such thing as too much data with these technologies – the more data they have, the better they learn and perform.
What would make retailers change their marketing systems?
In our survey of U.S. retail marketing executives, 67% said they would upgrade or change their systems if they could achieve faster, easier aggregation of customer data for targeting and analysis. Recognizing that systems can offer more than just data aggregation, 44% would upgrade or change if they could reduce the effort needed to manage marketing campaigns and optimize ROI.
Beyond rules-based algorithms for marketing personalization to AI
Until now, the optimal way to personalize offers and messages based on unique customer data was to leverage rules-based algorithms, which delivered significant benefits but required heavy manual involvement by personnel.
Based on our own experience, we have seen that applying AI-enabled capabilities, retailers can automate the process, rapidly increasing the scale, accuracy, timeliness and variety of offers they share with their customers.
In a pilot campaign, a grocery retailer optimized personalized offer allocation to 20% of its customer base, with the other 80% receiving rules-based personalized offers. After only three weeks, the retailer realized a 34% increase in the number of customers redeeming one or more offers, which resulted in a spike in overall campaign performance.